A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.
The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
The cancellation of bookings impact a hotel on various fronts:
The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.
Data Dictionary
import warnings
warnings.filterwarnings("ignore")
from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter("ignore", ConvergenceWarning)
# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np
# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)
# Library to split data
from sklearn.model_selection import train_test_split
# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
# To tune different models
from sklearn.model_selection import GridSearchCV
# To get diferent metric scores
from sklearn.metrics import (
f1_score,
accuracy_score,
recall_score,
precision_score,
confusion_matrix,
roc_auc_score,
ConfusionMatrixDisplay,
precision_recall_curve,
roc_curve,
make_scorer,
)
#from google.colab import drive
#drive.mount('/content/drive')
data=pd.read_csv("C:\\Users\\DELL\\Downloads\\INNHotelsGroup.csv")
data.head()
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | INN00001 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 224 | 2017 | 10 | 2 | Offline | 0 | 0 | 0 | 65.00000 | 0 | Not_Canceled |
| 1 | INN00002 | 2 | 0 | 2 | 3 | Not Selected | 0 | Room_Type 1 | 5 | 2018 | 11 | 6 | Online | 0 | 0 | 0 | 106.68000 | 1 | Not_Canceled |
| 2 | INN00003 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 2 | 28 | Online | 0 | 0 | 0 | 60.00000 | 0 | Canceled |
| 3 | INN00004 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 211 | 2018 | 5 | 20 | Online | 0 | 0 | 0 | 100.00000 | 0 | Canceled |
| 4 | INN00005 | 2 | 0 | 1 | 1 | Not Selected | 0 | Room_Type 1 | 48 | 2018 | 4 | 11 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
data.tail()
| Booking_ID | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 36270 | INN36271 | 3 | 0 | 2 | 6 | Meal Plan 1 | 0 | Room_Type 4 | 85 | 2018 | 8 | 3 | Online | 0 | 0 | 0 | 167.80000 | 1 | Not_Canceled |
| 36271 | INN36272 | 2 | 0 | 1 | 3 | Meal Plan 1 | 0 | Room_Type 1 | 228 | 2018 | 10 | 17 | Online | 0 | 0 | 0 | 90.95000 | 2 | Canceled |
| 36272 | INN36273 | 2 | 0 | 2 | 6 | Meal Plan 1 | 0 | Room_Type 1 | 148 | 2018 | 7 | 1 | Online | 0 | 0 | 0 | 98.39000 | 2 | Not_Canceled |
| 36273 | INN36274 | 2 | 0 | 0 | 3 | Not Selected | 0 | Room_Type 1 | 63 | 2018 | 4 | 21 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
| 36274 | INN36275 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 207 | 2018 | 12 | 30 | Offline | 0 | 0 | 0 | 161.67000 | 0 | Not_Canceled |
data.shape
(36275, 19)
There are 36,275 Rows and 19 Columns in the given data.
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 36275 entries, 0 to 36274 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Booking_ID 36275 non-null object 1 no_of_adults 36275 non-null int64 2 no_of_children 36275 non-null int64 3 no_of_weekend_nights 36275 non-null int64 4 no_of_week_nights 36275 non-null int64 5 type_of_meal_plan 36275 non-null object 6 required_car_parking_space 36275 non-null int64 7 room_type_reserved 36275 non-null object 8 lead_time 36275 non-null int64 9 arrival_year 36275 non-null int64 10 arrival_month 36275 non-null int64 11 arrival_date 36275 non-null int64 12 market_segment_type 36275 non-null object 13 repeated_guest 36275 non-null int64 14 no_of_previous_cancellations 36275 non-null int64 15 no_of_previous_bookings_not_canceled 36275 non-null int64 16 avg_price_per_room 36275 non-null float64 17 no_of_special_requests 36275 non-null int64 18 booking_status 36275 non-null object dtypes: float64(1), int64(13), object(5) memory usage: 5.3+ MB
data.isnull().sum().sum()
0
data.duplicated().sum()
0
data.nunique()
Booking_ID 36275 no_of_adults 5 no_of_children 6 no_of_weekend_nights 8 no_of_week_nights 18 type_of_meal_plan 4 required_car_parking_space 2 room_type_reserved 7 lead_time 352 arrival_year 2 arrival_month 12 arrival_date 31 market_segment_type 5 repeated_guest 2 no_of_previous_cancellations 9 no_of_previous_bookings_not_canceled 59 avg_price_per_room 3930 no_of_special_requests 6 booking_status 2 dtype: int64
data['market_segment_type'].value_counts(normalize=True)
Online 0.63994 Offline 0.29023 Corporate 0.05560 Complementary 0.01078 Aviation 0.00345 Name: market_segment_type, dtype: float64
Majority of guests have done Online Bookings.
data.drop('Booking_ID',axis=1,inplace=True)
data.head()
| no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | type_of_meal_plan | required_car_parking_space | room_type_reserved | lead_time | arrival_year | arrival_month | arrival_date | market_segment_type | repeated_guest | no_of_previous_cancellations | no_of_previous_bookings_not_canceled | avg_price_per_room | no_of_special_requests | booking_status | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 0 | 1 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 224 | 2017 | 10 | 2 | Offline | 0 | 0 | 0 | 65.00000 | 0 | Not_Canceled |
| 1 | 2 | 0 | 2 | 3 | Not Selected | 0 | Room_Type 1 | 5 | 2018 | 11 | 6 | Online | 0 | 0 | 0 | 106.68000 | 1 | Not_Canceled |
| 2 | 1 | 0 | 2 | 1 | Meal Plan 1 | 0 | Room_Type 1 | 1 | 2018 | 2 | 28 | Online | 0 | 0 | 0 | 60.00000 | 0 | Canceled |
| 3 | 2 | 0 | 0 | 2 | Meal Plan 1 | 0 | Room_Type 1 | 211 | 2018 | 5 | 20 | Online | 0 | 0 | 0 | 100.00000 | 0 | Canceled |
| 4 | 2 | 0 | 1 | 1 | Not Selected | 0 | Room_Type 1 | 48 | 2018 | 4 | 11 | Online | 0 | 0 | 0 | 94.50000 | 0 | Canceled |
The Booking id column is irrelevant to the classification problem so it has been dropped.
df=data.copy()
df["booking_status"] = df["booking_status"].apply(
lambda x: 1 if x == "Canceled" else 0
)
df.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| no_of_adults | 36275.00000 | 1.84496 | 0.51871 | 0.00000 | 2.00000 | 2.00000 | 2.00000 | 4.00000 |
| no_of_children | 36275.00000 | 0.10528 | 0.40265 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 10.00000 |
| no_of_weekend_nights | 36275.00000 | 0.81072 | 0.87064 | 0.00000 | 0.00000 | 1.00000 | 2.00000 | 7.00000 |
| no_of_week_nights | 36275.00000 | 2.20430 | 1.41090 | 0.00000 | 1.00000 | 2.00000 | 3.00000 | 17.00000 |
| required_car_parking_space | 36275.00000 | 0.03099 | 0.17328 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 |
| lead_time | 36275.00000 | 85.23256 | 85.93082 | 0.00000 | 17.00000 | 57.00000 | 126.00000 | 443.00000 |
| arrival_year | 36275.00000 | 2017.82043 | 0.38384 | 2017.00000 | 2018.00000 | 2018.00000 | 2018.00000 | 2018.00000 |
| arrival_month | 36275.00000 | 7.42365 | 3.06989 | 1.00000 | 5.00000 | 8.00000 | 10.00000 | 12.00000 |
| arrival_date | 36275.00000 | 15.59700 | 8.74045 | 1.00000 | 8.00000 | 16.00000 | 23.00000 | 31.00000 |
| repeated_guest | 36275.00000 | 0.02564 | 0.15805 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 1.00000 |
| no_of_previous_cancellations | 36275.00000 | 0.02335 | 0.36833 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 13.00000 |
| no_of_previous_bookings_not_canceled | 36275.00000 | 0.15341 | 1.75417 | 0.00000 | 0.00000 | 0.00000 | 0.00000 | 58.00000 |
| avg_price_per_room | 36275.00000 | 103.42354 | 35.08942 | 0.00000 | 80.30000 | 99.45000 | 120.00000 | 540.00000 |
| no_of_special_requests | 36275.00000 | 0.61966 | 0.78624 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 5.00000 |
| booking_status | 36275.00000 | 0.32764 | 0.46936 | 0.00000 | 0.00000 | 0.00000 | 1.00000 | 1.00000 |
Leading Questions:
def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (15,10))
kde: whether to show the density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a triangle will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
def labeled_barplot(data, feature,name, perc=False, n=None):
total = len(data[feature])
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 2, 4))
else:
plt.figure(figsize=(n + 2, 4))
plt.xticks(rotation=0, fontsize=10)
ax = sns.countplot(data=data,x=feature,palette="Paired",order=data[feature].value_counts().index[:n])
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(100 * p.get_height() / total)
else:
label = p.get_height()
x = p.get_x() + p.get_width() / 2
y = p.get_height()
ax.annotate(label,(x, y),ha="center",va="center",size=12,xytext=(0, 5),textcoords="offset points")
plt.title(name);
plt.show()
### function to plot distributions wrt target
def distribution_plot_wrt_target(data, predictor, target):
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
target_uniq = data[target].unique()
axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
sns.histplot(
data=data[data[target] == target_uniq[0]],
x=predictor,
kde=True,
ax=axs[0, 0],
color="teal",
stat="density",
)
axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
sns.histplot(
data=data[data[target] == target_uniq[1]],
x=predictor,
kde=True,
ax=axs[0, 1],
color="orange",
stat="density",
)
axs[1, 0].set_title("Boxplot w.r.t target")
sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")
axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
sns.boxplot(
data=data,
x=target,
y=predictor,
ax=axs[1, 1],
showfliers=False,
palette="gist_rainbow",
)
plt.tight_layout()
plt.show()
def stacked_barplot(data, predictor, target):
"""
Print the category counts and plot a stacked bar chart
data: dataframe
predictor: independent variable
target: target variable
"""
count = data[predictor].nunique()
sorter = data[target].value_counts().index[-1]
tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
by=sorter, ascending=False
)
print(tab1)
print("-" * 120)
tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
by=sorter, ascending=False
)
tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
plt.legend(
loc="lower left", frameon=False,
)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
plt.show()
histogram_boxplot(df, "avg_price_per_room")
df[df["avg_price_per_room"] == 0]
df.loc[df["avg_price_per_room"] == 0, "market_segment_type"].value_counts()
Complementary 354 Online 191 Name: market_segment_type, dtype: int64
# Calculating the 25th quantile
Q1 = df["avg_price_per_room"].quantile(0.25)
# Calculating the 75th quantile
Q3 = df["avg_price_per_room"].quantile(0.75) ## Complete the code to calculate 75th quantile for average price per room
# Calculating IQR
IQR = Q3 - Q1
# Calculating value of upper whisker
Upper_Whisker = Q3 + 1.5 * IQR
Upper_Whisker
179.55
df.loc[df["avg_price_per_room"] >= 500, "avg_price_per_room"] = Upper_Whisker
labeled_barplot(df, "no_of_adults","Number of Adults", perc=True)
Majority of around 72 % of bookings were made for 2 Adults.
labeled_barplot(df, "no_of_children","Number of Children", perc=True)
df['no_of_children'].value_counts()
0 33577 1 1618 2 1058 3 19 9 2 10 1 Name: no_of_children, dtype: int64
# replacing 9, and 10 children with 3
df["no_of_children"] = df["no_of_children"].replace([9, 10], 3)
labeled_barplot(df, "no_of_weekend_nights","Number of Weekend Nights", perc=True)
labeled_barplot(df, "no_of_week_nights","Number of Week Nights", perc=True)
labeled_barplot(df, "type_of_meal_plan","Meal Plan Type", perc=True)
df['type_of_meal_plan'].value_counts()
Meal Plan 1 27835 Not Selected 5130 Meal Plan 2 3305 Meal Plan 3 5 Name: type_of_meal_plan, dtype: int64
labeled_barplot(df, "required_car_parking_space","Car Parking Space", perc=True)
labeled_barplot(df, "room_type_reserved","Type of Room Reserved", perc=True)
histogram_boxplot(df, "lead_time")
labeled_barplot(df, "arrival_year","Arrival Year", perc=True)
labeled_barplot(df, "arrival_date","Arrival Date", perc=True)
histogram_boxplot(df, "no_of_previous_bookings_not_canceled")
histogram_boxplot(df, "no_of_previous_cancellations")
df['no_of_previous_cancellations'].value_counts()
0 35937 1 198 2 46 3 43 11 25 5 11 4 10 13 4 6 1 Name: no_of_previous_cancellations, dtype: int64
cols_list = df.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(12, 7))
sns.heatmap(df[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral");
sns.boxplot(df,x="repeated_guest",y="avg_price_per_room",showfliers=False,palette="Set1");
plt.xlabel('Repeated Guest')
plt.ylabel('Average Price per Room')
plt.title('Average Price vs Repeated Guest');
sns.boxplot(df,x="no_of_special_requests",y="avg_price_per_room",showfliers=False,palette="Set1");
plt.xlabel('NUmber of Special Request')
plt.ylabel('Average Price per Room')
plt.title('Average Price vs Special Request');
sns.lineplot(df,x="arrival_month",y="avg_price_per_room",ci=None);
plt.xlabel('Arrival Month')
plt.ylabel('Average Price per Room')
plt.title('Average Price vs Arrival Month');
distribution_plot_wrt_target(df, 'avg_price_per_room', 'booking_status')
distribution_plot_wrt_target(df, 'lead_time', 'booking_status')
df.booking_status.value_counts()
0 24390 1 11885 Name: booking_status, dtype: int64
family_data = df[(df["no_of_children"] >= 0) & (df["no_of_adults"] > 1)]
family_data.shape
(28441, 18)
family_data["no_of_family_members"] = (family_data["no_of_adults"] + family_data["no_of_children"])
stacked_barplot(family_data,'no_of_family_members','booking_status')
booking_status 0 1 All no_of_family_members All 18456 9985 28441 2 15506 8213 23719 3 2425 1368 3793 4 514 398 912 5 11 6 17 ------------------------------------------------------------------------------------------------------------------------
The booking status has an equal chance of getting cancelled no matter for how many Number of people it was booked.
Let's do a similar analysis for the customer who stay for at least a day at the hotel.
stay_data = df[(df["no_of_week_nights"] > 0) & (df["no_of_weekend_nights"] > 0)]
stay_data.shape
(17094, 18)
stay_data["total_days"] = (
stay_data["no_of_week_nights"] + stay_data["no_of_weekend_nights"]
)
stacked_barplot(stay_data, "total_days", "booking_status")
booking_status 0 1 All total_days All 10979 6115 17094 3 3689 2183 5872 4 2977 1387 4364 5 1593 738 2331 2 1301 639 1940 6 566 465 1031 7 590 383 973 8 100 79 179 10 51 58 109 9 58 53 111 14 5 27 32 15 5 26 31 13 3 15 18 12 9 15 24 11 24 15 39 20 3 8 11 19 1 5 6 16 1 5 6 17 1 4 5 18 0 3 3 21 1 3 4 22 0 2 2 23 1 1 2 24 0 1 1 ------------------------------------------------------------------------------------------------------------------------
There is an increase in chance of cancellation as the number of days of stay increases.
# grouping the data on arrival months and extracting the count of bookings
monthly_data = df.groupby(["arrival_month"])["booking_status"].count()
# creating a dataframe with months and count of customers in each month
monthly_data = pd.DataFrame(
{"Month": list(monthly_data.index), "Guests": list(monthly_data.values)}
)
# plotting the trend over different months
plt.figure(figsize=(10, 5))
sns.lineplot(data=monthly_data, x="Month", y="Guests")
plt.show()
There is an Upward trend in the number of guests with a peak in the month of October.
labeled_barplot(df, "arrival_month","Arrival Month", perc=True)
labeled_barplot(df, "market_segment_type","Market Segment ", perc=True)
plt.figure(figsize=(10, 6))
sns.boxplot(df, x="market_segment_type", y="avg_price_per_room",palette='Set3');
df.groupby('market_segment_type').agg({'avg_price_per_room':'mean'}).sort_values(by='avg_price_per_room',ascending=False).reset_index()
| market_segment_type | avg_price_per_room | |
|---|---|---|
| 0 | Online | 112.25685 |
| 1 | Aviation | 100.70400 |
| 2 | Offline | 91.59844 |
| 3 | Corporate | 82.91174 |
| 4 | Complementary | 3.14176 |
labeled_barplot(df, "booking_status","Booking Status", perc=True)
stacked_barplot(df, "repeated_guest", "booking_status")
booking_status 0 1 All repeated_guest All 24390 11885 36275 0 23476 11869 35345 1 914 16 930 ------------------------------------------------------------------------------------------------------------------------
df.groupby('repeated_guest')['booking_status'].value_counts(normalize=True)
repeated_guest booking_status
0 0 0.66420
1 0.33580
1 0 0.98280
1 0.01720
Name: booking_status, dtype: float64
stacked_barplot(df, "no_of_special_requests", "booking_status")
booking_status 0 1 All no_of_special_requests All 24390 11885 36275 0 11232 8545 19777 1 8670 2703 11373 2 3727 637 4364 3 675 0 675 4 78 0 78 5 8 0 8 ------------------------------------------------------------------------------------------------------------------------
df.isnull().sum()
no_of_adults 0 no_of_children 0 no_of_weekend_nights 0 no_of_week_nights 0 type_of_meal_plan 0 required_car_parking_space 0 room_type_reserved 0 lead_time 0 arrival_year 0 arrival_month 0 arrival_date 0 market_segment_type 0 repeated_guest 0 no_of_previous_cancellations 0 no_of_previous_bookings_not_canceled 0 avg_price_per_room 0 no_of_special_requests 0 booking_status 0 dtype: int64
#Outlier detection using boxplot
numerical_cols = df.select_dtypes(include=np.number).columns.tolist()
#Dropping booking status
numerical_cols.remove('booking_status')
#Formula for outlier detection
plt.figure(figsize=(15,12))
for i, variable in enumerate(numerical_cols):
plt.subplot(4,4,i+1)
plt.boxplot(df[variable],whis=1.5)
plt.tight_layout()
plt.title(variable)
plt.show()
#Independent and dependent variables being defined
x = df.drop(['booking_status'],axis=1)
y = df['booking_status']
#Adding constant
X = sm.add_constant(x)
X = pd.get_dummies(X,drop_first=True)
#Create a train and test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)
print('Shape of the training set:',X_train.shape)
print('Shape of test set:', X_test.shape)
print('Percentage of Classes in Training Set:',y_train.value_counts(normalize=True))
print('Percentage of Classes in Test Set:',y_test.value_counts(normalize=True))
Shape of the training set: (25392, 28) Shape of test set: (10883, 28) Percentage of Classes in Training Set: 0 0.67064 1 0.32936 Name: booking_status, dtype: float64 Percentage of Classes in Test Set: 0 0.67638 1 0.32362 Name: booking_status, dtype: float64
# fitting logistic regression model
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit(disp=False) ## Complete the code to fit logistic regression
print(lg.summary()) ## Complete the code to print summary of the model
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25364
Method: MLE Df Model: 27
Date: Sun, 18 Feb 2024 Pseudo R-squ.: 0.3292
Time: 22:42:55 Log-Likelihood: -10794.
converged: False LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
========================================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------------
const -922.8266 120.832 -7.637 0.000 -1159.653 -686.000
no_of_adults 0.1137 0.038 3.019 0.003 0.040 0.188
no_of_children 0.1580 0.062 2.544 0.011 0.036 0.280
no_of_weekend_nights 0.1067 0.020 5.395 0.000 0.068 0.145
no_of_week_nights 0.0397 0.012 3.235 0.001 0.016 0.064
required_car_parking_space -1.5943 0.138 -11.565 0.000 -1.865 -1.324
lead_time 0.0157 0.000 58.863 0.000 0.015 0.016
arrival_year 0.4561 0.060 7.617 0.000 0.339 0.573
arrival_month -0.0417 0.006 -6.441 0.000 -0.054 -0.029
arrival_date 0.0005 0.002 0.259 0.796 -0.003 0.004
repeated_guest -2.3472 0.617 -3.806 0.000 -3.556 -1.139
no_of_previous_cancellations 0.2664 0.086 3.108 0.002 0.098 0.434
no_of_previous_bookings_not_canceled -0.1727 0.153 -1.131 0.258 -0.472 0.127
avg_price_per_room 0.0188 0.001 25.396 0.000 0.017 0.020
no_of_special_requests -1.4689 0.030 -48.782 0.000 -1.528 -1.410
type_of_meal_plan_Meal Plan 2 0.1756 0.067 2.636 0.008 0.045 0.306
type_of_meal_plan_Meal Plan 3 17.3584 3987.836 0.004 0.997 -7798.656 7833.373
type_of_meal_plan_Not Selected 0.2784 0.053 5.247 0.000 0.174 0.382
room_type_reserved_Room_Type 2 -0.3605 0.131 -2.748 0.006 -0.618 -0.103
room_type_reserved_Room_Type 3 -0.0012 1.310 -0.001 0.999 -2.568 2.566
room_type_reserved_Room_Type 4 -0.2823 0.053 -5.304 0.000 -0.387 -0.178
room_type_reserved_Room_Type 5 -0.7189 0.209 -3.438 0.001 -1.129 -0.309
room_type_reserved_Room_Type 6 -0.9501 0.151 -6.274 0.000 -1.247 -0.653
room_type_reserved_Room_Type 7 -1.4003 0.294 -4.770 0.000 -1.976 -0.825
market_segment_type_Complementary -40.5975 5.65e+05 -7.19e-05 1.000 -1.11e+06 1.11e+06
market_segment_type_Corporate -1.1924 0.266 -4.483 0.000 -1.714 -0.671
market_segment_type_Offline -2.1946 0.255 -8.621 0.000 -2.694 -1.696
market_segment_type_Online -0.3995 0.251 -1.590 0.112 -0.892 0.093
========================================================================================================
Negative values of the coefficient show that the probability of a booking getting cancelled decreases with the increase of the corresponding attribute value.
Positive values of the coefficient show that the probability ofbooking getting cancelled increases with the increase of the corresponding attribute value.
p-value of a variable indicates if the variable is significant or not. If we consider the significance level to be 0.05 (5%), then any variable with a p-value less than 0.05 would be considered significant.
Both the cases are important as:
If we predict that a booking will not be canceled and the booking gets canceled then the hotel will lose resources and will have to bear additional costs of distribution channels.
If we predict that a booking will get canceled and the booking doesn't get canceled the hotel might not be able to provide satisfactory services to the customer by assuming that this booking will be canceled. This might damage the brand equity.
F1 Score to be maximized, greater the F1 score higher are the chances of minimizing False Negatives and False Positives.# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
model, predictors, target, threshold=0.5
):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
# checking which probabilities are greater than threshold
pred_temp = model.predict(predictors) > threshold
# rounding off the above values to get classes
pred = np.round(pred_temp)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
# defining a function to plot the confusion_matrix of a classification model
def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
threshold: threshold for classifying the observation as class 1
"""
y_pred = model.predict(predictors) > threshold
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("True label")
plt.xlabel("Predicted label")
print("Training performance:")
model_performance_classification_statsmodels(lg, X_train, y_train)
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80600 | 0.63410 | 0.73971 | 0.68285 |
def checking_vif(predictors):
vif = pd.DataFrame()
vif["feature"] = predictors.columns
# calculating VIF for each feature
vif["VIF"] = [
variance_inflation_factor(predictors.values, i)
for i in range(len(predictors.columns))
]
return vif
checking_vif(X_train)
| feature | VIF | |
|---|---|---|
| 0 | const | 39497686.20788 |
| 1 | no_of_adults | 1.35113 |
| 2 | no_of_children | 2.09358 |
| 3 | no_of_weekend_nights | 1.06948 |
| 4 | no_of_week_nights | 1.09571 |
| 5 | required_car_parking_space | 1.03997 |
| 6 | lead_time | 1.39517 |
| 7 | arrival_year | 1.43190 |
| 8 | arrival_month | 1.27633 |
| 9 | arrival_date | 1.00679 |
| 10 | repeated_guest | 1.78358 |
| 11 | no_of_previous_cancellations | 1.39569 |
| 12 | no_of_previous_bookings_not_canceled | 1.65200 |
| 13 | avg_price_per_room | 2.06860 |
| 14 | no_of_special_requests | 1.24798 |
| 15 | type_of_meal_plan_Meal Plan 2 | 1.27328 |
| 16 | type_of_meal_plan_Meal Plan 3 | 1.02526 |
| 17 | type_of_meal_plan_Not Selected | 1.27306 |
| 18 | room_type_reserved_Room_Type 2 | 1.10595 |
| 19 | room_type_reserved_Room_Type 3 | 1.00330 |
| 20 | room_type_reserved_Room_Type 4 | 1.36361 |
| 21 | room_type_reserved_Room_Type 5 | 1.02800 |
| 22 | room_type_reserved_Room_Type 6 | 2.05614 |
| 23 | room_type_reserved_Room_Type 7 | 1.11816 |
| 24 | market_segment_type_Complementary | 4.50276 |
| 25 | market_segment_type_Corporate | 16.92829 |
| 26 | market_segment_type_Offline | 64.11564 |
| 27 | market_segment_type_Online | 71.18026 |
#Initial list of columns
cols = X_train.columns.tolist()
#Setting an initial max p-value
max_p_value = 1
while len(cols) > 0:
#Defining the train set
x_train_aux = X_train[cols]
#Fitting the model
model = sm.Logit(y_train, x_train_aux).fit(disp=False)
#Getting the p-values and the maximum p-value
p_values = model.pvalues
max_p_value = max(p_values)
#Name of the variable with maximum p-value
feature_with_p_max = p_values.idxmax()
if max_p_value > 0.05:
cols.remove(feature_with_p_max)
else:
break
selected_features = cols
print(selected_features)
['const', 'no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Corporate', 'market_segment_type_Offline']
X_train1 = X_train[selected_features]
X_test1 = X_test[selected_features]
logit1 = sm.Logit(y_train, X_train1.astype(float))
lg1 = logit1.fit(disp=False)
print(lg1.summary())
Logit Regression Results
==============================================================================
Dep. Variable: booking_status No. Observations: 25392
Model: Logit Df Residuals: 25370
Method: MLE Df Model: 21
Date: Sun, 18 Feb 2024 Pseudo R-squ.: 0.3282
Time: 22:42:57 Log-Likelihood: -10810.
converged: True LL-Null: -16091.
Covariance Type: nonrobust LLR p-value: 0.000
==================================================================================================
coef std err z P>|z| [0.025 0.975]
--------------------------------------------------------------------------------------------------
const -915.6391 120.471 -7.600 0.000 -1151.758 -679.520
no_of_adults 0.1088 0.037 2.914 0.004 0.036 0.182
no_of_children 0.1531 0.062 2.470 0.014 0.032 0.275
no_of_weekend_nights 0.1086 0.020 5.498 0.000 0.070 0.147
no_of_week_nights 0.0417 0.012 3.399 0.001 0.018 0.066
required_car_parking_space -1.5947 0.138 -11.564 0.000 -1.865 -1.324
lead_time 0.0157 0.000 59.213 0.000 0.015 0.016
arrival_year 0.4523 0.060 7.576 0.000 0.335 0.569
arrival_month -0.0425 0.006 -6.591 0.000 -0.055 -0.030
repeated_guest -2.7367 0.557 -4.916 0.000 -3.828 -1.646
no_of_previous_cancellations 0.2288 0.077 2.983 0.003 0.078 0.379
avg_price_per_room 0.0192 0.001 26.336 0.000 0.018 0.021
no_of_special_requests -1.4698 0.030 -48.884 0.000 -1.529 -1.411
type_of_meal_plan_Meal Plan 2 0.1642 0.067 2.469 0.014 0.034 0.295
type_of_meal_plan_Not Selected 0.2860 0.053 5.406 0.000 0.182 0.390
room_type_reserved_Room_Type 2 -0.3552 0.131 -2.709 0.007 -0.612 -0.098
room_type_reserved_Room_Type 4 -0.2828 0.053 -5.330 0.000 -0.387 -0.179
room_type_reserved_Room_Type 5 -0.7364 0.208 -3.535 0.000 -1.145 -0.328
room_type_reserved_Room_Type 6 -0.9682 0.151 -6.403 0.000 -1.265 -0.672
room_type_reserved_Room_Type 7 -1.4343 0.293 -4.892 0.000 -2.009 -0.860
market_segment_type_Corporate -0.7913 0.103 -7.692 0.000 -0.993 -0.590
market_segment_type_Offline -1.7854 0.052 -34.363 0.000 -1.887 -1.684
==================================================================================================
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1, y_train)
print("Training performance:")
log_model_train_perf=model_performance_classification_statsmodels(lg1, X_train1, y_train)
log_model_train_perf
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80545 | 0.63267 | 0.73907 | 0.68174 |
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test)
print("Test set performance:")
log_reg_model_test_perf=model_performance_classification_statsmodels(lg1, X_test1, y_test)
log_reg_model_test_perf
Test set performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80465 | 0.63089 | 0.72900 | 0.67641 |
# converting coefficients to odds
odds = np.exp(lg1.params)
# finding the percentage change
perc_change_odds = (np.exp(lg1.params) - 1) * 100
# removing limit from number of columns to display
pd.set_option("display.max_columns", None)
# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train1.columns).T
| const | no_of_adults | no_of_children | no_of_weekend_nights | no_of_week_nights | required_car_parking_space | lead_time | arrival_year | arrival_month | repeated_guest | no_of_previous_cancellations | avg_price_per_room | no_of_special_requests | type_of_meal_plan_Meal Plan 2 | type_of_meal_plan_Not Selected | room_type_reserved_Room_Type 2 | room_type_reserved_Room_Type 4 | room_type_reserved_Room_Type 5 | room_type_reserved_Room_Type 6 | room_type_reserved_Room_Type 7 | market_segment_type_Corporate | market_segment_type_Offline | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Odds | 0.00000 | 1.11491 | 1.16546 | 1.11470 | 1.04258 | 0.20296 | 1.01583 | 1.57195 | 0.95839 | 0.06478 | 1.25712 | 1.01937 | 0.22996 | 1.17846 | 1.33109 | 0.70104 | 0.75364 | 0.47885 | 0.37977 | 0.23827 | 0.45326 | 0.16773 |
| Change_odd% | -100.00000 | 11.49096 | 16.54593 | 11.46966 | 4.25841 | -79.70395 | 1.58331 | 57.19508 | -4.16120 | -93.52180 | 25.71181 | 1.93684 | -77.00374 | 17.84641 | 33.10947 | -29.89588 | -24.63551 | -52.11548 | -62.02290 | -76.17294 | -54.67373 | -83.22724 |
logit_roc_auc_train = roc_auc_score(y_train, lg1.predict(X_train1))
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
Logistic Regression model is giving a good performance on training set.
# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))
optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)
0.3700522558707991
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc)
# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.79265 | 0.73622 | 0.66808 | 0.70049 |
logit_roc_auc_train = roc_auc_score(y_test, lg1.predict(X_test1))
fpr, tpr, thresholds = roc_curve(y_test, lg1.predict(X_test1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc)
# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.79555 | 0.73964 | 0.66573 | 0.70074 |
y_scores = lg1.predict(X_train1)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)
def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
plt.plot(thresholds, precisions[:-1], "b--", label="precision")
plt.plot(thresholds, recalls[:-1], "g--", label="recall")
plt.xlabel("Threshold")
plt.legend(loc="upper left")
plt.ylim([0, 1])
plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()
# setting the threshold
optimal_threshold_curve = 0.42
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1, y_train, threshold=optimal_threshold_curve)
log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
lg1, X_train1, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve
Training performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80132 | 0.69939 | 0.69797 | 0.69868 |
# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test, threshold=optimal_threshold_curve)
log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
lg1, X_test1, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve
Test performance:
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.80345 | 0.70358 | 0.69353 | 0.69852 |
#Model Comparison Training Set
models_train_comp_df = pd.concat(
[
log_model_train_perf.T,
log_reg_model_train_perf_threshold_auc_roc.T,
log_reg_model_train_perf_threshold_curve.T,
],
axis=1,
)
models_train_comp_df.columns = [
"Logistic Regression-default Threshold",
"Logistic Regression-0.37 Threshold",
"Logistic Regression-0.42 Threshold",
]
print("Training performance comparison:")
models_train_comp_df
Training performance comparison:
| Logistic Regression-default Threshold | Logistic Regression-0.37 Threshold | Logistic Regression-0.42 Threshold | |
|---|---|---|---|
| Accuracy | 0.80545 | 0.79265 | 0.80132 |
| Recall | 0.63267 | 0.73622 | 0.69939 |
| Precision | 0.73907 | 0.66808 | 0.69797 |
| F1 | 0.68174 | 0.70049 | 0.69868 |
# test performance comparison
models_test_comp_df = pd.concat(
[
log_reg_model_test_perf.T,
log_reg_model_test_perf_threshold_auc_roc.T,
log_reg_model_test_perf_threshold_curve.T,
],
axis=1,
)
models_test_comp_df.columns = [
"Logistic Regression-default Threshold",
"Logistic Regression-0.37 Threshold",
"Logistic Regression-0.42 Threshold",
]
print("Test performance comparison:")
models_test_comp_df
Test performance comparison:
| Logistic Regression-default Threshold | Logistic Regression-0.37 Threshold | Logistic Regression-0.42 Threshold | |
|---|---|---|---|
| Accuracy | 0.80465 | 0.79555 | 0.80345 |
| Recall | 0.63089 | 0.73964 | 0.70358 |
| Precision | 0.72900 | 0.66573 | 0.69353 |
| F1 | 0.67641 | 0.70074 | 0.69852 |
#Creating independent and dependent variables
X = df.drop(['booking_status'],axis=1)
Y = df['booking_status']
#Create dummy variables
X = pd.get_dummies(X, drop_first=True)
#Splitting for training and test data
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=1)
print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))
Shape of Training set : (25392, 27) Shape of test set : (10883, 27) Percentage of classes in training set: 0 0.67064 1 0.32936 Name: booking_status, dtype: float64 Percentage of classes in test set: 0 0.67638 1 0.32362 Name: booking_status, dtype: float64
# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
"""
Function to compute different metrics to check classification model performance
model: classifier
predictors: independent variables
target: dependent variable
"""
# predicting using the independent variables
pred = model.predict(predictors)
acc = accuracy_score(target, pred) # to compute Accuracy
recall = recall_score(target, pred) # to compute Recall
precision = precision_score(target, pred) # to compute Precision
f1 = f1_score(target, pred) # to compute F1-score
# creating a dataframe of metrics
df_perf = pd.DataFrame(
{"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
index=[0],
)
return df_perf
def confusion_matrix_sklearn(model, predictors, target):
"""
To plot the confusion_matrix with percentages
model: classifier
predictors: independent variables
target: dependent variable
"""
y_pred = model.predict(predictors)
cm = confusion_matrix(target, y_pred)
labels = np.asarray(
[
["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
for item in cm.flatten()
]
).reshape(2, 2)
plt.figure(figsize=(6, 4))
sns.heatmap(cm, annot=labels, fmt="")
plt.ylabel("True label")
plt.xlabel("Predicted label")
model = DecisionTreeClassifier(random_state=1)
model.fit(X_train, y_train)
DecisionTreeClassifier(random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(random_state=1)
confusion_matrix_sklearn(model,X_train,y_train)
decision_tree_perf_train_default = model_performance_classification_sklearn(model, X_train, y_train)
decision_tree_perf_train_default
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.99421 | 0.98661 | 0.99578 | 0.99117 |
confusion_matrix_sklearn(model,X_test,y_test)
decision_tree_perf_test_default = model_performance_classification_sklearn(model,X_test,y_test)
decision_tree_perf_test_default
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.87118 | 0.81175 | 0.79461 | 0.80309 |
The Model is performing better on Training set than on the Testing set indicating an Overfitting Model.
model = DecisionTreeClassifier(random_state=1, class_weight="balanced")
model.fit(X_train, y_train)
DecisionTreeClassifier(class_weight='balanced', random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(class_weight='balanced', random_state=1)
confusion_matrix_sklearn(model, X_train, y_train)
decision_tree_perf_train = model_performance_classification_sklearn(
model, X_train, y_train
)
decision_tree_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.99311 | 0.99510 | 0.98415 | 0.98960 |
confusion_matrix_sklearn(model, X_test, y_test)
decision_tree_perf_test = model_performance_classification_sklearn(
model, X_test, y_test
)
decision_tree_perf_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.86621 | 0.80494 | 0.78663 | 0.79568 |
The Model is still performing better on Training set than on the Testing set indicating an Overfitting Model with class weight adjustment.
feature_names = list(X_train.columns)
importances = model.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
As the Model is Overfitting we will try to reduce it by Pruning Techniques.
# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1, class_weight="balanced")
# Grid of parameters to choose from
parameters = {
"max_depth": np.arange(2, 7, 2),
"max_leaf_nodes": [50, 75, 150, 250],
"min_samples_split": [10, 30, 50, 70],
}
# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(f1_score)
# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)
# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_
# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)
DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
min_samples_split=10, random_state=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
min_samples_split=10, random_state=1)confusion_matrix_sklearn(estimator, X_train, y_train)
decision_tree_tune_perf_train = model_performance_classification_sklearn(
estimator, X_train, y_train
)
decision_tree_tune_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.83097 | 0.78608 | 0.72425 | 0.75390 |
confusion_matrix_sklearn(estimator, X_test, y_test)
decision_tree_tune_perf_test = model_performance_classification_sklearn(
estimator, X_test, y_test
)
decision_tree_tune_perf_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.83497 | 0.78336 | 0.72758 | 0.75444 |
The model is giving a generalized result now since the f1 scores on both the train and test data are coming to be around 0.75 which shows that the model is able to generalize well on unseen data.
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
estimator,
feature_names=feature_names,
filled=True,
fontsize=9,
node_ids=False,
class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
arrow = o.arrow_patch
if arrow is not None:
arrow.set_edgecolor("black")
arrow.set_linewidth(1)
plt.show()
# Text report showing the rules of a decision tree -
print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_weekend_nights <= 0.50 | | | | | |--- avg_price_per_room <= 196.50 | | | | | | |--- weights: [1736.39, 133.59] class: 0 | | | | | |--- avg_price_per_room > 196.50 | | | | | | |--- weights: [0.75, 24.29] class: 1 | | | | |--- no_of_weekend_nights > 0.50 | | | | | |--- lead_time <= 68.50 | | | | | | |--- weights: [960.27, 223.16] class: 0 | | | | | |--- lead_time > 68.50 | | | | | | |--- weights: [129.73, 160.92] class: 1 | | | |--- lead_time > 90.50 | | | | |--- lead_time <= 117.50 | | | | | |--- avg_price_per_room <= 93.58 | | | | | | |--- weights: [214.72, 227.72] class: 1 | | | | | |--- avg_price_per_room > 93.58 | | | | | | |--- weights: [82.76, 285.41] class: 1 | | | | |--- lead_time > 117.50 | | | | | |--- no_of_week_nights <= 1.50 | | | | | | |--- weights: [87.23, 81.98] class: 0 | | | | | |--- no_of_week_nights > 1.50 | | | | | | |--- weights: [228.14, 48.58] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- avg_price_per_room <= 99.44 | | | | | |--- arrival_month <= 1.50 | | | | | | |--- weights: [92.45, 0.00] class: 0 | | | | | |--- arrival_month > 1.50 | | | | | | |--- weights: [363.83, 132.08] class: 0 | | | | |--- avg_price_per_room > 99.44 | | | | | |--- lead_time <= 3.50 | | | | | | |--- weights: [219.94, 85.01] class: 0 | | | | | |--- lead_time > 3.50 | | | | | | |--- weights: [132.71, 280.85] class: 1 | | | |--- lead_time > 13.50 | | | | |--- required_car_parking_space <= 0.50 | | | | | |--- avg_price_per_room <= 71.92 | | | | | | |--- weights: [158.80, 159.40] class: 1 | | | | | |--- avg_price_per_room > 71.92 | | | | | | |--- weights: [850.67, 3543.28] class: 1 | | | | |--- required_car_parking_space > 0.50 | | | | | |--- weights: [48.46, 1.52] class: 0 | |--- no_of_special_requests > 0.50 | | |--- no_of_special_requests <= 1.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- lead_time <= 102.50 | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | |--- weights: [697.09, 9.11] class: 0 | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | |--- weights: [15.66, 9.11] class: 0 | | | | |--- lead_time > 102.50 | | | | | |--- no_of_week_nights <= 2.50 | | | | | | |--- weights: [32.06, 19.74] class: 0 | | | | | |--- no_of_week_nights > 2.50 | | | | | | |--- weights: [44.73, 3.04] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- lead_time <= 8.50 | | | | | |--- lead_time <= 4.50 | | | | | | |--- weights: [498.03, 44.03] class: 0 | | | | | |--- lead_time > 4.50 | | | | | | |--- weights: [258.71, 63.76] class: 0 | | | | |--- lead_time > 8.50 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- weights: [2512.51, 1451.32] class: 0 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- weights: [134.20, 1.52] class: 0 | | |--- no_of_special_requests > 1.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_week_nights <= 3.50 | | | | | |--- weights: [1585.04, 0.00] class: 0 | | | | |--- no_of_week_nights > 3.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- weights: [180.42, 57.69] class: 0 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [52.19, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- no_of_special_requests <= 2.50 | | | | | |--- arrival_month <= 8.50 | | | | | | |--- weights: [184.90, 56.17] class: 0 | | | | | |--- arrival_month > 8.50 | | | | | | |--- weights: [106.61, 106.27] class: 0 | | | | |--- no_of_special_requests > 2.50 | | | | | |--- weights: [67.10, 0.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- no_of_special_requests <= 0.50 | | | |--- no_of_adults <= 1.50 | | | | |--- market_segment_type_Online <= 0.50 | | | | | |--- lead_time <= 163.50 | | | | | | |--- weights: [3.73, 24.29] class: 1 | | | | | |--- lead_time > 163.50 | | | | | | |--- weights: [257.96, 62.24] class: 0 | | | | |--- market_segment_type_Online > 0.50 | | | | | |--- avg_price_per_room <= 2.50 | | | | | | |--- weights: [8.95, 3.04] class: 0 | | | | | |--- avg_price_per_room > 2.50 | | | | | | |--- weights: [0.75, 97.16] class: 1 | | | |--- no_of_adults > 1.50 | | | | |--- avg_price_per_room <= 82.47 | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | |--- weights: [2.98, 282.37] class: 1 | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | |--- weights: [213.97, 385.60] class: 1 | | | | |--- avg_price_per_room > 82.47 | | | | | |--- no_of_adults <= 2.50 | | | | | | |--- weights: [23.86, 1030.80] class: 1 | | | | | |--- no_of_adults > 2.50 | | | | | | |--- weights: [5.22, 0.00] class: 0 | | |--- no_of_special_requests > 0.50 | | | |--- no_of_weekend_nights <= 0.50 | | | | |--- lead_time <= 180.50 | | | | | |--- lead_time <= 159.50 | | | | | | |--- weights: [7.46, 7.59] class: 1 | | | | | |--- lead_time > 159.50 | | | | | | |--- weights: [37.28, 4.55] class: 0 | | | | |--- lead_time > 180.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- weights: [20.13, 212.54] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | |--- no_of_weekend_nights > 0.50 | | | | |--- market_segment_type_Offline <= 0.50 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- weights: [231.12, 110.82] class: 0 | | | | | |--- arrival_month > 11.50 | | | | | | |--- weights: [19.38, 34.92] class: 1 | | | | |--- market_segment_type_Offline > 0.50 | | | | | |--- lead_time <= 348.50 | | | | | | |--- weights: [106.61, 3.04] class: 0 | | | | | |--- lead_time > 348.50 | | | | | | |--- weights: [5.96, 4.55] class: 0 | |--- avg_price_per_room > 100.04 | | |--- arrival_month <= 11.50 | | | |--- no_of_special_requests <= 2.50 | | | | |--- weights: [0.00, 3200.19] class: 1 | | | |--- no_of_special_requests > 2.50 | | | | |--- weights: [23.11, 0.00] class: 0 | | |--- arrival_month > 11.50 | | | |--- no_of_special_requests <= 0.50 | | | | |--- weights: [35.04, 0.00] class: 0 | | | |--- no_of_special_requests > 0.50 | | | | |--- arrival_date <= 24.50 | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | |--- arrival_date > 24.50 | | | | | |--- weights: [3.73, 22.77] class: 1
# importance of features in the tree building
importances = estimator.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities
pd.DataFrame(path)
| ccp_alphas | impurities | |
|---|---|---|
| 0 | 0.00000 | 0.00838 |
| 1 | 0.00000 | 0.00838 |
| 2 | 0.00000 | 0.00838 |
| 3 | 0.00000 | 0.00838 |
| 4 | 0.00000 | 0.00838 |
| ... | ... | ... |
| 1839 | 0.00890 | 0.32806 |
| 1840 | 0.00980 | 0.33786 |
| 1841 | 0.01272 | 0.35058 |
| 1842 | 0.03412 | 0.41882 |
| 1843 | 0.08118 | 0.50000 |
1844 rows × 2 columns
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()
clfs = []
for ccp_alpha in ccp_alphas:
clf = DecisionTreeClassifier(
random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
)
clf.fit(X_train, y_train)
clfs.append(clf)
print(
"Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
clfs[-1].tree_.node_count, ccp_alphas[-1]
)
)
Number of nodes in the last tree is: 1 with ccp_alpha: 0.0811791438913696
clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]
node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()
f1_train = []
for clf in clfs:
pred_train = clf.predict(X_train)
values_train = f1_score(y_train, pred_train)
f1_train.append(values_train)
f1_test = []
for clf in clfs:
pred_test = clf.predict(X_test)
values_test = f1_score(y_test, pred_test)
f1_test.append(values_test)
fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("F1 Score")
ax.set_title("F1 Score vs alpha for training and testing sets")
ax.plot(ccp_alphas, f1_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, f1_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()
index_best_model = np.argmax(f1_test)
best_model = clfs[index_best_model]
print(best_model)
DecisionTreeClassifier(ccp_alpha=0.00012267633155167043,
class_weight='balanced', random_state=1)
confusion_matrix_sklearn(best_model, X_train, y_train)
decision_tree_post_perf_train = model_performance_classification_sklearn(
best_model, X_train, y_train
)
decision_tree_post_perf_train
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.89954 | 0.90303 | 0.81274 | 0.85551 |
confusion_matrix_sklearn(best_model, X_test, y_test)
decision_tree_post_test = model_performance_classification_sklearn(
best_model, X_test, y_test
)
decision_tree_post_test
| Accuracy | Recall | Precision | F1 | |
|---|---|---|---|---|
| 0 | 0.86879 | 0.85576 | 0.76614 | 0.80848 |
plt.figure(figsize=(20, 10))
out = tree.plot_tree(
best_model,
feature_names=feature_names,
filled=True,
fontsize=9,
node_ids=False,
class_names=None,
)
for o in out:
arrow = o.arrow_patch
if arrow is not None:
arrow.set_edgecolor("black")
arrow.set_linewidth(1)
plt.show()
# Text report showing the rules of a decision tree -
print(tree.export_text(best_model, feature_names=feature_names, show_weights=True))
|--- lead_time <= 151.50 | |--- no_of_special_requests <= 0.50 | | |--- market_segment_type_Online <= 0.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_weekend_nights <= 0.50 | | | | | |--- avg_price_per_room <= 196.50 | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | |--- lead_time <= 16.50 | | | | | | | | |--- avg_price_per_room <= 68.50 | | | | | | | | | |--- weights: [207.26, 10.63] class: 0 | | | | | | | | |--- avg_price_per_room > 68.50 | | | | | | | | | |--- arrival_date <= 29.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | | |--- arrival_date > 29.50 | | | | | | | | | | |--- weights: [2.24, 7.59] class: 1 | | | | | | | |--- lead_time > 16.50 | | | | | | | | |--- avg_price_per_room <= 135.00 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- no_of_previous_bookings_not_canceled <= 0.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_previous_bookings_not_canceled > 0.50 | | | | | | | | | | | |--- weights: [11.18, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [21.62, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 135.00 | | | | | | | | | |--- weights: [0.00, 12.14] class: 1 | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | |--- weights: [1199.59, 1.52] class: 0 | | | | | |--- avg_price_per_room > 196.50 | | | | | | |--- weights: [0.75, 24.29] class: 1 | | | | |--- no_of_weekend_nights > 0.50 | | | | | |--- lead_time <= 68.50 | | | | | | |--- arrival_month <= 9.50 | | | | | | | |--- avg_price_per_room <= 63.29 | | | | | | | | |--- arrival_date <= 20.50 | | | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | | | |--- weights: [41.75, 0.00] class: 0 | | | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | | | | |--- arrival_date > 20.50 | | | | | | | | | |--- avg_price_per_room <= 59.75 | | | | | | | | | | |--- arrival_date <= 23.50 | | | | | | | | | | | |--- weights: [1.49, 12.14] class: 1 | | | | | | | | | | |--- arrival_date > 23.50 | | | | | | | | | | | |--- weights: [14.91, 1.52] class: 0 | | | | | | | | | |--- avg_price_per_room > 59.75 | | | | | | | | | | |--- lead_time <= 44.00 | | | | | | | | | | | |--- weights: [0.75, 59.21] class: 1 | | | | | | | | | | |--- lead_time > 44.00 | | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 63.29 | | | | | | | | |--- no_of_weekend_nights <= 3.50 | | | | | | | | | |--- lead_time <= 59.50 | | | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- lead_time > 59.50 | | | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | | | |--- weights: [20.13, 0.00] class: 0 | | | | | | | | |--- no_of_weekend_nights > 3.50 | | | | | | | | | |--- weights: [0.75, 15.18] class: 1 | | | | | | |--- arrival_month > 9.50 | | | | | | | |--- weights: [413.04, 27.33] class: 0 | | | | | |--- lead_time > 68.50 | | | | | | |--- avg_price_per_room <= 99.98 | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | |--- avg_price_per_room <= 62.50 | | | | | | | | | |--- weights: [15.66, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 62.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- weights: [8.20, 25.81] class: 1 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | |--- arrival_month > 3.50 | | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | | |--- weights: [55.17, 3.04] class: 0 | | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | | |--- lead_time <= 73.50 | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | | |--- lead_time > 73.50 | | | | | | | | | | |--- weights: [21.62, 4.55] class: 0 | | | | | | |--- avg_price_per_room > 99.98 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- avg_price_per_room <= 132.43 | | | | | | | | | |--- weights: [9.69, 122.97] class: 1 | | | | | | | | |--- avg_price_per_room > 132.43 | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- lead_time <= 117.50 | | | | | |--- avg_price_per_room <= 93.58 | | | | | | |--- avg_price_per_room <= 75.07 | | | | | | | |--- no_of_week_nights <= 2.50 | | | | | | | | |--- avg_price_per_room <= 58.75 | | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 58.75 | | | | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | | | | |--- weights: [4.47, 0.00] class: 0 | | | | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | | | |--- weights: [2.24, 118.41] class: 1 | | | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | |--- no_of_week_nights > 2.50 | | | | | | | | |--- arrival_date <= 11.50 | | | | | | | | | |--- weights: [31.31, 0.00] class: 0 | | | | | | | | |--- arrival_date > 11.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | | |--- weights: [23.11, 6.07] class: 0 | | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | | |--- weights: [5.96, 9.11] class: 1 | | | | | | |--- avg_price_per_room > 75.07 | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | |--- weights: [59.64, 3.04] class: 0 | | | | | | | |--- arrival_month > 3.50 | | | | | | | | |--- arrival_month <= 4.50 | | | | | | | | | |--- weights: [1.49, 16.70] class: 1 | | | | | | | | |--- arrival_month > 4.50 | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | |--- avg_price_per_room <= 86.00 | | | | | | | | | | | |--- weights: [2.24, 16.70] class: 1 | | | | | | | | | | |--- avg_price_per_room > 86.00 | | | | | | | | | | | |--- weights: [8.95, 3.04] class: 0 | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | |--- arrival_date <= 22.50 | | | | | | | | | | | |--- weights: [44.73, 4.55] class: 0 | | | | | | | | | | |--- arrival_date > 22.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | |--- avg_price_per_room > 93.58 | | | | | | |--- arrival_date <= 11.50 | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | |--- weights: [16.40, 39.47] class: 1 | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | |--- weights: [20.13, 6.07] class: 0 | | | | | | |--- arrival_date > 11.50 | | | | | | | |--- avg_price_per_room <= 102.09 | | | | | | | | |--- weights: [5.22, 144.22] class: 1 | | | | | | | |--- avg_price_per_room > 102.09 | | | | | | | | |--- avg_price_per_room <= 109.50 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [0.75, 16.70] class: 1 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- weights: [33.55, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 109.50 | | | | | | | | | |--- avg_price_per_room <= 124.25 | | | | | | | | | | |--- weights: [2.98, 75.91] class: 1 | | | | | | | | | |--- avg_price_per_room > 124.25 | | | | | | | | | | |--- weights: [3.73, 3.04] class: 0 | | | | |--- lead_time > 117.50 | | | | | |--- no_of_week_nights <= 1.50 | | | | | | |--- arrival_date <= 7.50 | | | | | | | |--- weights: [38.02, 0.00] class: 0 | | | | | | |--- arrival_date > 7.50 | | | | | | | |--- avg_price_per_room <= 93.58 | | | | | | | | |--- avg_price_per_room <= 65.38 | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | |--- avg_price_per_room > 65.38 | | | | | | | | | |--- weights: [24.60, 3.04] class: 0 | | | | | | | |--- avg_price_per_room > 93.58 | | | | | | | | |--- arrival_date <= 28.00 | | | | | | | | | |--- weights: [14.91, 72.87] class: 1 | | | | | | | | |--- arrival_date > 28.00 | | | | | | | | | |--- weights: [9.69, 1.52] class: 0 | | | | | |--- no_of_week_nights > 1.50 | | | | | | |--- no_of_adults <= 1.50 | | | | | | | |--- weights: [84.25, 0.00] class: 0 | | | | | | |--- no_of_adults > 1.50 | | | | | | | |--- lead_time <= 125.50 | | | | | | | | |--- avg_price_per_room <= 90.85 | | | | | | | | | |--- avg_price_per_room <= 87.50 | | | | | | | | | | |--- weights: [13.42, 13.66] class: 1 | | | | | | | | | |--- avg_price_per_room > 87.50 | | | | | | | | | | |--- weights: [0.00, 15.18] class: 1 | | | | | | | | |--- avg_price_per_room > 90.85 | | | | | | | | | |--- weights: [10.44, 0.00] class: 0 | | | | | | | |--- lead_time > 125.50 | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | |--- weights: [58.15, 18.22] class: 0 | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | |--- weights: [61.88, 1.52] class: 0 | | |--- market_segment_type_Online > 0.50 | | | |--- lead_time <= 13.50 | | | | |--- avg_price_per_room <= 99.44 | | | | | |--- arrival_month <= 1.50 | | | | | | |--- weights: [92.45, 0.00] class: 0 | | | | | |--- arrival_month > 1.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | |--- avg_price_per_room <= 70.05 | | | | | | | | | |--- weights: [31.31, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 70.05 | | | | | | | | | |--- lead_time <= 5.50 | | | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | | | |--- weights: [38.77, 1.52] class: 0 | | | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | |--- lead_time > 5.50 | | | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | | | |--- weights: [34.30, 40.99] class: 1 | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | |--- no_of_adults <= 1.50 | | | | | | | | | |--- weights: [0.00, 19.74] class: 1 | | | | | | | | |--- no_of_adults > 1.50 | | | | | | | | | |--- lead_time <= 2.50 | | | | | | | | | | |--- avg_price_per_room <= 74.21 | | | | | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | | | | | | |--- avg_price_per_room > 74.21 | | | | | | | | | | | |--- weights: [9.69, 0.00] class: 0 | | | | | | | | | |--- lead_time > 2.50 | | | | | | | | | | |--- weights: [4.47, 10.63] class: 1 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | |--- weights: [155.07, 6.07] class: 0 | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- weights: [3.73, 10.63] class: 1 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [7.46, 0.00] class: 0 | | | | |--- avg_price_per_room > 99.44 | | | | | |--- lead_time <= 3.50 | | | | | | |--- avg_price_per_room <= 202.67 | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | |--- weights: [63.37, 30.36] class: 0 | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | |--- arrival_date <= 20.50 | | | | | | | | | | |--- weights: [115.56, 12.14] class: 0 | | | | | | | | | |--- arrival_date > 20.50 | | | | | | | | | | |--- arrival_date <= 24.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_date > 24.50 | | | | | | | | | | | |--- weights: [28.33, 3.04] class: 0 | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | |--- weights: [0.00, 6.07] class: 1 | | | | | | |--- avg_price_per_room > 202.67 | | | | | | | |--- weights: [0.75, 22.77] class: 1 | | | | | |--- lead_time > 3.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- avg_price_per_room <= 119.25 | | | | | | | | |--- avg_price_per_room <= 118.50 | | | | | | | | | |--- weights: [18.64, 59.21] class: 1 | | | | | | | | |--- avg_price_per_room > 118.50 | | | | | | | | | |--- weights: [8.20, 1.52] class: 0 | | | | | | | |--- avg_price_per_room > 119.25 | | | | | | | | |--- weights: [34.30, 171.55] class: 1 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- weights: [26.09, 1.52] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_date <= 14.00 | | | | | | | | | | |--- weights: [9.69, 36.43] class: 1 | | | | | | | | | |--- arrival_date > 14.00 | | | | | | | | | | |--- avg_price_per_room <= 208.67 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 208.67 | | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [15.66, 0.00] class: 0 | | | |--- lead_time > 13.50 | | | | |--- required_car_parking_space <= 0.50 | | | | | |--- avg_price_per_room <= 71.92 | | | | | | |--- avg_price_per_room <= 59.43 | | | | | | | |--- lead_time <= 84.50 | | | | | | | | |--- weights: [50.70, 7.59] class: 0 | | | | | | | |--- lead_time > 84.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_date <= 27.00 | | | | | | | | | | |--- lead_time <= 131.50 | | | | | | | | | | | |--- weights: [0.75, 15.18] class: 1 | | | | | | | | | | |--- lead_time > 131.50 | | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | | | |--- arrival_date > 27.00 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- weights: [10.44, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 59.43 | | | | | | | |--- lead_time <= 25.50 | | | | | | | | |--- weights: [20.88, 6.07] class: 0 | | | | | | | |--- lead_time > 25.50 | | | | | | | | |--- avg_price_per_room <= 71.34 | | | | | | | | | |--- arrival_month <= 3.50 | | | | | | | | | | |--- lead_time <= 68.50 | | | | | | | | | | | |--- weights: [15.66, 78.94] class: 1 | | | | | | | | | | |--- lead_time > 68.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- arrival_month > 3.50 | | | | | | | | | | |--- lead_time <= 102.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 102.00 | | | | | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | | | | | |--- avg_price_per_room > 71.34 | | | | | | | | | |--- weights: [11.18, 0.00] class: 0 | | | | | |--- avg_price_per_room > 71.92 | | | | | | |--- arrival_year <= 2017.50 | | | | | | | |--- lead_time <= 65.50 | | | | | | | | |--- avg_price_per_room <= 120.45 | | | | | | | | | |--- weights: [79.77, 9.11] class: 0 | | | | | | | | |--- avg_price_per_room > 120.45 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- weights: [3.73, 12.14] class: 1 | | | | | | | |--- lead_time > 65.50 | | | | | | | | |--- type_of_meal_plan_Meal Plan 2 <= 0.50 | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | |--- weights: [16.40, 47.06] class: 1 | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- type_of_meal_plan_Meal Plan 2 > 0.50 | | | | | | | | | |--- weights: [0.00, 63.76] class: 1 | | | | | | |--- arrival_year > 2017.50 | | | | | | | |--- avg_price_per_room <= 104.31 | | | | | | | | |--- lead_time <= 25.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | | |--- weights: [16.40, 0.00] class: 0 | | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | | |--- weights: [38.77, 118.41] class: 1 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- weights: [23.11, 0.00] class: 0 | | | | | | | | |--- lead_time > 25.50 | | | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | | |--- weights: [39.51, 185.21] class: 1 | | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | | | |--- weights: [73.81, 411.41] class: 1 | | | | | | | |--- avg_price_per_room > 104.31 | | | | | | | | |--- arrival_month <= 10.50 | | | | | | | | | |--- room_type_reserved_Room_Type 5 <= 0.50 | | | | | | | | | | |--- avg_price_per_room <= 195.30 | | | | | | | | | | | |--- truncated branch of depth 9 | | | | | | | | | | |--- avg_price_per_room > 195.30 | | | | | | | | | | | |--- weights: [0.75, 138.15] class: 1 | | | | | | | | | |--- room_type_reserved_Room_Type 5 > 0.50 | | | | | | | | | | |--- arrival_date <= 22.50 | | | | | | | | | | | |--- weights: [11.18, 6.07] class: 0 | | | | | | | | | | |--- arrival_date > 22.50 | | | | | | | | | | | |--- weights: [0.75, 9.11] class: 1 | | | | | | | | |--- arrival_month > 10.50 | | | | | | | | | |--- avg_price_per_room <= 168.06 | | | | | | | | | | |--- lead_time <= 22.00 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 22.00 | | | | | | | | | | | |--- weights: [17.15, 83.50] class: 1 | | | | | | | | | |--- avg_price_per_room > 168.06 | | | | | | | | | | |--- weights: [12.67, 6.07] class: 0 | | | | |--- required_car_parking_space > 0.50 | | | | | |--- weights: [48.46, 1.52] class: 0 | |--- no_of_special_requests > 0.50 | | |--- no_of_special_requests <= 1.50 | | | |--- market_segment_type_Online <= 0.50 | | | | |--- lead_time <= 102.50 | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | |--- weights: [697.09, 9.11] class: 0 | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | |--- lead_time <= 63.00 | | | | | | | |--- weights: [15.66, 1.52] class: 0 | | | | | | |--- lead_time > 63.00 | | | | | | | |--- weights: [0.00, 7.59] class: 1 | | | | |--- lead_time > 102.50 | | | | | |--- no_of_week_nights <= 2.50 | | | | | | |--- lead_time <= 105.00 | | | | | | | |--- weights: [0.75, 6.07] class: 1 | | | | | | |--- lead_time > 105.00 | | | | | | | |--- weights: [31.31, 13.66] class: 0 | | | | | |--- no_of_week_nights > 2.50 | | | | | | |--- weights: [44.73, 3.04] class: 0 | | | |--- market_segment_type_Online > 0.50 | | | | |--- lead_time <= 8.50 | | | | | |--- lead_time <= 4.50 | | | | | | |--- no_of_week_nights <= 10.00 | | | | | | | |--- weights: [498.03, 40.99] class: 0 | | | | | | |--- no_of_week_nights > 10.00 | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | |--- lead_time > 4.50 | | | | | | |--- arrival_date <= 13.50 | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | |--- weights: [58.90, 36.43] class: 0 | | | | | | | |--- arrival_month > 9.50 | | | | | | | | |--- weights: [33.55, 1.52] class: 0 | | | | | | |--- arrival_date > 13.50 | | | | | | | |--- type_of_meal_plan_Not Selected <= 0.50 | | | | | | | | |--- weights: [123.76, 9.11] class: 0 | | | | | | | |--- type_of_meal_plan_Not Selected > 0.50 | | | | | | | | |--- avg_price_per_room <= 126.33 | | | | | | | | | |--- weights: [32.80, 3.04] class: 0 | | | | | | | | |--- avg_price_per_room > 126.33 | | | | | | | | | |--- weights: [9.69, 13.66] class: 1 | | | | |--- lead_time > 8.50 | | | | | |--- required_car_parking_space <= 0.50 | | | | | | |--- avg_price_per_room <= 118.55 | | | | | | | |--- lead_time <= 61.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_month <= 1.50 | | | | | | | | | | |--- weights: [70.08, 0.00] class: 0 | | | | | | | | | |--- arrival_month > 1.50 | | | | | | | | | | |--- no_of_week_nights <= 4.50 | | | | | | | | | | | |--- truncated branch of depth 11 | | | | | | | | | | |--- no_of_week_nights > 4.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [126.74, 1.52] class: 0 | | | | | | | |--- lead_time > 61.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | | |--- weights: [4.47, 57.69] class: 1 | | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | | |--- lead_time <= 66.50 | | | | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 66.50 | | | | | | | | | | | |--- truncated branch of depth 5 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- avg_price_per_room <= 71.93 | | | | | | | | | | | |--- weights: [54.43, 3.04] class: 0 | | | | | | | | | | |--- avg_price_per_room > 71.93 | | | | | | | | | | | |--- truncated branch of depth 10 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | |--- avg_price_per_room > 118.55 | | | | | | | |--- arrival_month <= 8.50 | | | | | | | | |--- arrival_date <= 19.50 | | | | | | | | | |--- no_of_week_nights <= 7.50 | | | | | | | | | | |--- avg_price_per_room <= 177.15 | | | | | | | | | | | |--- truncated branch of depth 6 | | | | | | | | | | |--- avg_price_per_room > 177.15 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- no_of_week_nights > 7.50 | | | | | | | | | | |--- weights: [0.00, 6.07] class: 1 | | | | | | | | |--- arrival_date > 19.50 | | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | | |--- avg_price_per_room <= 121.20 | | | | | | | | | | | |--- weights: [18.64, 6.07] class: 0 | | | | | | | | | | |--- avg_price_per_room > 121.20 | | | | | | | | | | | |--- truncated branch of depth 4 | | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | | |--- lead_time <= 55.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- lead_time > 55.50 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | |--- arrival_month > 8.50 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | |--- weights: [11.93, 10.63] class: 0 | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | |--- weights: [37.28, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | |--- avg_price_per_room <= 119.20 | | | | | | | | | | | |--- weights: [9.69, 28.84] class: 1 | | | | | | | | | | |--- avg_price_per_room > 119.20 | | | | | | | | | | | |--- truncated branch of depth 12 | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | |--- lead_time <= 100.00 | | | | | | | | | | | |--- weights: [49.95, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 100.00 | | | | | | | | | | | |--- weights: [0.75, 18.22] class: 1 | | | | | |--- required_car_parking_space > 0.50 | | | | | | |--- weights: [134.20, 1.52] class: 0 | | |--- no_of_special_requests > 1.50 | | | |--- lead_time <= 90.50 | | | | |--- no_of_week_nights <= 3.50 | | | | | |--- weights: [1585.04, 0.00] class: 0 | | | | |--- no_of_week_nights > 3.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- no_of_week_nights <= 9.50 | | | | | | | |--- lead_time <= 6.50 | | | | | | | | |--- weights: [32.06, 0.00] class: 0 | | | | | | | |--- lead_time > 6.50 | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | |--- arrival_date <= 5.50 | | | | | | | | | | |--- weights: [23.11, 1.52] class: 0 | | | | | | | | | |--- arrival_date > 5.50 | | | | | | | | | | |--- avg_price_per_room <= 93.09 | | | | | | | | | | | |--- truncated branch of depth 2 | | | | | | | | | | |--- avg_price_per_room > 93.09 | | | | | | | | | | | |--- weights: [77.54, 27.33] class: 0 | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | |--- weights: [19.38, 0.00] class: 0 | | | | | | |--- no_of_week_nights > 9.50 | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [52.19, 0.00] class: 0 | | | |--- lead_time > 90.50 | | | | |--- no_of_special_requests <= 2.50 | | | | | |--- arrival_month <= 8.50 | | | | | | |--- avg_price_per_room <= 202.95 | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | |--- arrival_month <= 7.50 | | | | | | | | | |--- weights: [1.49, 9.11] class: 1 | | | | | | | | |--- arrival_month > 7.50 | | | | | | | | | |--- weights: [8.20, 3.04] class: 0 | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | |--- lead_time <= 150.50 | | | | | | | | | |--- weights: [175.20, 28.84] class: 0 | | | | | | | | |--- lead_time > 150.50 | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | |--- avg_price_per_room > 202.95 | | | | | | | |--- weights: [0.00, 10.63] class: 1 | | | | | |--- arrival_month > 8.50 | | | | | | |--- avg_price_per_room <= 153.15 | | | | | | | |--- room_type_reserved_Room_Type 2 <= 0.50 | | | | | | | | |--- avg_price_per_room <= 71.12 | | | | | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | | | | | |--- avg_price_per_room > 71.12 | | | | | | | | | |--- avg_price_per_room <= 90.42 | | | | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- arrival_month > 11.50 | | | | | | | | | | | |--- weights: [12.67, 7.59] class: 0 | | | | | | | | | |--- avg_price_per_room > 90.42 | | | | | | | | | | |--- weights: [64.12, 60.72] class: 0 | | | | | | | |--- room_type_reserved_Room_Type 2 > 0.50 | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | |--- avg_price_per_room > 153.15 | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | |--- no_of_special_requests > 2.50 | | | | | |--- weights: [67.10, 0.00] class: 0 |--- lead_time > 151.50 | |--- avg_price_per_room <= 100.04 | | |--- no_of_special_requests <= 0.50 | | | |--- no_of_adults <= 1.50 | | | | |--- market_segment_type_Online <= 0.50 | | | | | |--- lead_time <= 163.50 | | | | | | |--- arrival_month <= 5.00 | | | | | | | |--- weights: [2.98, 0.00] class: 0 | | | | | | |--- arrival_month > 5.00 | | | | | | | |--- weights: [0.75, 24.29] class: 1 | | | | | |--- lead_time > 163.50 | | | | | | |--- lead_time <= 341.00 | | | | | | | |--- lead_time <= 173.00 | | | | | | | | |--- arrival_date <= 3.50 | | | | | | | | | |--- weights: [46.97, 9.11] class: 0 | | | | | | | | |--- arrival_date > 3.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.00 | | | | | | | | | | |--- weights: [0.00, 13.66] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 1.00 | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | |--- lead_time > 173.00 | | | | | | | | |--- arrival_month <= 5.50 | | | | | | | | | |--- arrival_date <= 7.50 | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | | | |--- arrival_date > 7.50 | | | | | | | | | | |--- weights: [6.71, 0.00] class: 0 | | | | | | | | |--- arrival_month > 5.50 | | | | | | | | | |--- weights: [188.62, 7.59] class: 0 | | | | | | |--- lead_time > 341.00 | | | | | | | |--- weights: [13.42, 27.33] class: 1 | | | | |--- market_segment_type_Online > 0.50 | | | | | |--- avg_price_per_room <= 2.50 | | | | | | |--- lead_time <= 285.50 | | | | | | | |--- weights: [8.20, 0.00] class: 0 | | | | | | |--- lead_time > 285.50 | | | | | | | |--- weights: [0.75, 3.04] class: 1 | | | | | |--- avg_price_per_room > 2.50 | | | | | | |--- weights: [0.75, 97.16] class: 1 | | | |--- no_of_adults > 1.50 | | | | |--- avg_price_per_room <= 82.47 | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | |--- weights: [2.98, 282.37] class: 1 | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | |--- arrival_month <= 11.50 | | | | | | | |--- lead_time <= 244.00 | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | |--- no_of_weekend_nights <= 1.50 | | | | | | | | | | |--- lead_time <= 166.50 | | | | | | | | | | | |--- weights: [2.24, 0.00] class: 0 | | | | | | | | | | |--- lead_time > 166.50 | | | | | | | | | | | |--- weights: [2.24, 57.69] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 1.50 | | | | | | | | | | |--- weights: [17.89, 0.00] class: 0 | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | |--- no_of_weekend_nights <= 0.50 | | | | | | | | | | |--- arrival_month <= 9.50 | | | | | | | | | | | |--- weights: [11.18, 3.04] class: 0 | | | | | | | | | | |--- arrival_month > 9.50 | | | | | | | | | | | |--- weights: [0.00, 12.14] class: 1 | | | | | | | | | |--- no_of_weekend_nights > 0.50 | | | | | | | | | | |--- weights: [75.30, 12.14] class: 0 | | | | | | | |--- lead_time > 244.00 | | | | | | | | |--- arrival_year <= 2017.50 | | | | | | | | | |--- weights: [25.35, 0.00] class: 0 | | | | | | | | |--- arrival_year > 2017.50 | | | | | | | | | |--- avg_price_per_room <= 80.38 | | | | | | | | | | |--- no_of_week_nights <= 3.50 | | | | | | | | | | | |--- weights: [11.18, 264.15] class: 1 | | | | | | | | | | |--- no_of_week_nights > 3.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- avg_price_per_room > 80.38 | | | | | | | | | | |--- weights: [7.46, 0.00] class: 0 | | | | | | |--- arrival_month > 11.50 | | | | | | | |--- weights: [46.22, 0.00] class: 0 | | | | |--- avg_price_per_room > 82.47 | | | | | |--- no_of_adults <= 2.50 | | | | | | |--- lead_time <= 324.50 | | | | | | | |--- arrival_month <= 11.50 | | | | | | | | |--- room_type_reserved_Room_Type 4 <= 0.50 | | | | | | | | | |--- weights: [7.46, 986.78] class: 1 | | | | | | | | |--- room_type_reserved_Room_Type 4 > 0.50 | | | | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | | | | |--- weights: [0.00, 10.63] class: 1 | | | | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | | | | |--- weights: [4.47, 0.00] class: 0 | | | | | | | |--- arrival_month > 11.50 | | | | | | | | |--- market_segment_type_Offline <= 0.50 | | | | | | | | | |--- weights: [0.00, 19.74] class: 1 | | | | | | | | |--- market_segment_type_Offline > 0.50 | | | | | | | | | |--- weights: [5.22, 0.00] class: 0 | | | | | | |--- lead_time > 324.50 | | | | | | | |--- avg_price_per_room <= 89.00 | | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | | |--- avg_price_per_room > 89.00 | | | | | | | | |--- weights: [0.75, 13.66] class: 1 | | | | | |--- no_of_adults > 2.50 | | | | | | |--- weights: [5.22, 0.00] class: 0 | | |--- no_of_special_requests > 0.50 | | | |--- no_of_weekend_nights <= 0.50 | | | | |--- lead_time <= 180.50 | | | | | |--- lead_time <= 159.50 | | | | | | |--- arrival_month <= 8.50 | | | | | | | |--- weights: [5.96, 0.00] class: 0 | | | | | | |--- arrival_month > 8.50 | | | | | | | |--- weights: [1.49, 7.59] class: 1 | | | | | |--- lead_time > 159.50 | | | | | | |--- arrival_date <= 1.50 | | | | | | | |--- weights: [1.49, 3.04] class: 1 | | | | | | |--- arrival_date > 1.50 | | | | | | | |--- weights: [35.79, 1.52] class: 0 | | | | |--- lead_time > 180.50 | | | | | |--- no_of_special_requests <= 2.50 | | | | | | |--- market_segment_type_Online <= 0.50 | | | | | | | |--- no_of_adults <= 2.50 | | | | | | | | |--- weights: [12.67, 3.04] class: 0 | | | | | | | |--- no_of_adults > 2.50 | | | | | | | | |--- weights: [0.00, 3.04] class: 1 | | | | | | |--- market_segment_type_Online > 0.50 | | | | | | | |--- weights: [7.46, 206.46] class: 1 | | | | | |--- no_of_special_requests > 2.50 | | | | | | |--- weights: [8.95, 0.00] class: 0 | | | |--- no_of_weekend_nights > 0.50 | | | | |--- market_segment_type_Offline <= 0.50 | | | | | |--- arrival_month <= 11.50 | | | | | | |--- avg_price_per_room <= 76.48 | | | | | | | |--- weights: [46.97, 4.55] class: 0 | | | | | | |--- avg_price_per_room > 76.48 | | | | | | | |--- no_of_week_nights <= 6.50 | | | | | | | | |--- arrival_date <= 27.50 | | | | | | | | | |--- lead_time <= 233.00 | | | | | | | | | | |--- lead_time <= 152.50 | | | | | | | | | | | |--- weights: [1.49, 4.55] class: 1 | | | | | | | | | | |--- lead_time > 152.50 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | |--- lead_time > 233.00 | | | | | | | | | | |--- weights: [23.11, 19.74] class: 0 | | | | | | | | |--- arrival_date > 27.50 | | | | | | | | | |--- no_of_week_nights <= 1.50 | | | | | | | | | | |--- weights: [2.24, 15.18] class: 1 | | | | | | | | | |--- no_of_week_nights > 1.50 | | | | | | | | | | |--- lead_time <= 269.00 | | | | | | | | | | | |--- truncated branch of depth 3 | | | | | | | | | | |--- lead_time > 269.00 | | | | | | | | | | | |--- weights: [0.00, 4.55] class: 1 | | | | | | | |--- no_of_week_nights > 6.50 | | | | | | | | |--- weights: [4.47, 13.66] class: 1 | | | | | |--- arrival_month > 11.50 | | | | | | |--- arrival_date <= 14.50 | | | | | | | |--- weights: [8.20, 3.04] class: 0 | | | | | | |--- arrival_date > 14.50 | | | | | | | |--- weights: [11.18, 31.88] class: 1 | | | | |--- market_segment_type_Offline > 0.50 | | | | | |--- lead_time <= 348.50 | | | | | | |--- weights: [106.61, 3.04] class: 0 | | | | | |--- lead_time > 348.50 | | | | | | |--- weights: [5.96, 4.55] class: 0 | |--- avg_price_per_room > 100.04 | | |--- arrival_month <= 11.50 | | | |--- no_of_special_requests <= 2.50 | | | | |--- weights: [0.00, 3200.19] class: 1 | | | |--- no_of_special_requests > 2.50 | | | | |--- weights: [23.11, 0.00] class: 0 | | |--- arrival_month > 11.50 | | | |--- no_of_special_requests <= 0.50 | | | | |--- weights: [35.04, 0.00] class: 0 | | | |--- no_of_special_requests > 0.50 | | | | |--- arrival_date <= 24.50 | | | | | |--- weights: [3.73, 0.00] class: 0 | | | | |--- arrival_date > 24.50 | | | | | |--- weights: [3.73, 22.77] class: 1
importances = best_model.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()
# training performance comparison
models_test_comp_df = pd.concat(
[
decision_tree_perf_train_default.T,
decision_tree_perf_train.T,
decision_tree_tune_perf_train.T,
decision_tree_post_perf_train.T,
],
axis=1,
)
models_test_comp_df.columns = [
"Decision Tree without class_weight",
"Decision Tree with class_weight",
"Decision Tree (Pre-Pruning)",
"Decision Tree (Post-Pruning)",
]
print("Train set performance comparison:")
models_test_comp_df
Train set performance comparison:
| Decision Tree without class_weight | Decision Tree with class_weight | Decision Tree (Pre-Pruning) | Decision Tree (Post-Pruning) | |
|---|---|---|---|---|
| Accuracy | 0.99421 | 0.99311 | 0.83097 | 0.89954 |
| Recall | 0.98661 | 0.99510 | 0.78608 | 0.90303 |
| Precision | 0.99578 | 0.98415 | 0.72425 | 0.81274 |
| F1 | 0.99117 | 0.98960 | 0.75390 | 0.85551 |
# testing performance comparison
models_test_comp_df = pd.concat(
[
decision_tree_perf_test_default.T,
decision_tree_perf_test.T,
decision_tree_tune_perf_test.T,
decision_tree_post_test.T,
],
axis=1,
)
models_test_comp_df.columns = [
"Decision Tree without class_weight",
"Decision Tree with class_weight",
"Decision Tree (Pre-Pruning)",
"Decision Tree (Post-Pruning)",
]
print("Test set performance comparison:")
models_test_comp_df
Test set performance comparison:
| Decision Tree without class_weight | Decision Tree with class_weight | Decision Tree (Pre-Pruning) | Decision Tree (Post-Pruning) | |
|---|---|---|---|---|
| Accuracy | 0.87118 | 0.86621 | 0.83497 | 0.86879 |
| Recall | 0.81175 | 0.80494 | 0.78336 | 0.85576 |
| Precision | 0.79461 | 0.78663 | 0.72758 | 0.76614 |
| F1 | 0.80309 | 0.79568 | 0.75444 | 0.80848 |